The inertial navigation system (INS) is a widely used technology for guidance and navigation. The INS is composed of an inertial measurement unit (IMU) and a processor wherein an IMU contains accelerometers and gyroscopes which are inertial sensors detecting platform motion with respect to an inertial coordinate system. An important advantage of the INS is independence from external support, such as positional signals from artificial satellites, however, it cannot maintain high accuracy for long distance by itself because of accumulating sensor errors over time.
More recent development in global positioning system (GPS) has enabled low-cost navigation without growing error. The GPS, however, involves occasional large multipath errors in urban canyons (i.e., urban areas surrounded by high rise buildings) and signal dropouts inside buildings or tunnels. Therefore, efforts have been made to develop integrated INS/GPS navigation systems by combining the GPS and INS using a Kalman filter algorithm to remedy the performance problems in both systems.
Inertial sensors (accelerometers and gyroscopes) for an IMU used to be expensive and large, thus only used in high precision applications, for example, aerospace and military navigation. To establish an IMU with compact packaging and an inexpensive manner, efforts have been made to develop micro-electro mechanical system (MEMS) sensors, resulting in commercialization of low-cost and small inertial sensors. However, MEMS sensors involve large bias and noise. Low cost MEMS sensors have been largely adopted by cost sensitive navigation products such as automotive and portable navigation systems. In integrated MEMS IMU/GPS navigation systems, however, errors quickly accumulate into a large amount as soon as GPS signals drop out due to buildings, tunnels, etc.
Bye et al. suggested, in U.S. Pat. No. 6,859,727 entitled “ATTITUDE CHANGE KALMAN FILTER MEASUREMENT APPARATUS AND METHOD”, in Col. 4, lines 61-62, a Kalman filter based calibration method as “for a non-rotating IMU, the externally observed attitude or heading change (at the aiding source) is taken to be zero”. When this concept is applied as a condition of zero side velocity of a ground vehicle, divergent navigation solutions due to erroneous MEMS sensors are largely improved to be non-divergent solutions with reasonable positioning accuracy. However, as discovered by the inventor of this application, vehicle's side velocity has an analytical non-zero term. Suppressing the non-zero velocity into zero will cause unfavorable side effects such as a shortage of the total velocity and erroneous increment of pitch angle estimate, which results in a large positioning error when GPS signals are lost.
Therefore, there is a need for a new navigation system and method using low-cost MEMS IMU with capability of maintaining high accuracy even when GPS is lost for a long period of time by evaluating not only constant values but also non-constant and non-zero analytical conditions.